Moth Detection from Pheromone Trap Images Using Deep Learning Object Detectors

Suk-Ju Hong, Sang-Yeon Kim, Eungchan Kim, Chang-Hyup Lee, Jung-Sup Lee, Dong-Soo Lee, Jiwoong Bang and Ghiseok Kim
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Suk-Ju Hong: Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Sang-Yeon Kim: Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Eungchan Kim: Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Chang-Hyup Lee: Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Jung-Sup Lee: Protected Horticulture Research Institute, National Institute of Horticultural and Herbal Science, Rural Development Administration, 1425, Jinham-ro, Haman-myeon, Haman-gun, Gyeongsangnam-do 52054, Korea
Dong-Soo Lee: Department of Leaders in Industry-University Cooperation, Chung-Ang University, 4726, Seodong-daero, Daedeok-myeon, Anseong-si, Gyeonggi-do 17546, Korea
Jiwoong Bang: Protected Horticulture Research Institute, National Institute of Horticultural and Herbal Science, Rural Development Administration, 1425, Jinham-ro, Haman-myeon, Haman-gun, Gyeongsangnam-do 52054, Korea
Ghiseok Kim: Department of Biosystems and Biomaterials Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea

Agriculture, 2020, vol. 10, issue 5, 1-12

Abstract: Diverse pheromones and pheromone-based traps, as well as images acquired from insects captured by pheromone-based traps, have been studied and developed to monitor the presence and abundance of pests and to protect plants. The purpose of this study is to construct models that detect three species of pest moths in pheromone trap images using deep learning object detection methods and compare their speed and accuracy. Moth images in pheromone traps were collected for training and evaluation of deep learning detectors. Collected images were then subjected to a labeling process that defines the ground truths of target objects for their box locations and classes. Because there were a few negative objects in the dataset, non-target insects were labeled as unknown class and images of non-target insects were added to the dataset. Moreover, data augmentation methods were applied to the training process, and parameters of detectors that were pre-trained with the COCO dataset were used as initial parameter values. Seven detectors—Faster R-CNN ResNet 101, Faster R-CNN ResNet 50, Faster R-CNN Inception v.2, R-FCN ResNet 101, Retinanet ResNet 50, Retinanet Mobile v.2, and SSD Inception v.2 were trained and evaluated. Faster R-CNN ResNet 101 detector exhibited the highest accuracy (mAP as 90.25), and seven different detector types showed different accuracy and speed. Furthermore, when unexpected insects were included in the collected images, a four-class detector with an unknown class (non-target insect) showed lower detection error than a three-class detector.

Keywords: pheromone trap; pest; moth; deep learning; horticulture; insect detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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